Traditional choropleth maps, created on the basis of administrative units, often fail to accurately represent population distribution due to the high spatial heterogeneity and the temporal dynamics of the population within the units. Furthermore, updating the data of spatial population statistics is time-consuming and costly, which underlies the relative lack of high-resolution and high-quality population data for implementing or validating population modeling work, in particular in low- and middle-income countries (LMIC). Dasymetric modeling has become an important technique to produce high-resolution gridded population surfaces. In this study, carried out in Dhaka City, Bangladesh, dasymetric mapping was implemented with the assistance of a combination of an object-based image analysis method (for generating ancillary data) and Geographically Weighted Regression (for improving the accuracy of the dasymetric modeling on the basis of building use). Buildings were extracted from WorldView 2 imagery as ancillary data, and a building-based GWR model was selected as the final model to disaggregate population counts from administrative units onto 5 m raster cells. The overall accuracy of the image classification was 77.75%, but the root mean square error (RMSE) of the building-based GWR model for the population disaggregation was significantly less compared to the RMSE values of GWR based land use, Ordinary Least Square based land use and building modeling. Our model has potential to be adapted to other LMIC countries, where high-quality ground-truth population data are lacking. With increasingly available satellite data, the approach developed in this study can facilitate high-resolution population modeling in a complex urban setting, and hence improve the demographic, social, environmental and health research in LMICs.

中文翻译：

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基于高分辨率遥感数据的地理加权回归最优人口建模方法-以孟加拉国达卡市为例

在行政单位的基础上创建的传统choropleth图，由于高度的空间异质性和单位内人口的时间动态，常常无法准确地表示人口分布。此外，更新空间人口统计数据非常耗时且成本高昂，这是相对缺乏用于实施或验证人口建模工作的高分辨率和高质量人口数据的原因，特别是在中低收入国家（ LMIC）。对称建模已成为生成高分辨率网格化总体表面的一项重要技术。在孟加拉国达卡市进行的这项研究中，通过基于对象的图像分析方法（用于生成辅助数据）和地理加权回归（用于在建筑物使用的基础上提高dasymetric建模的准确性）的组合，实现了dasymetric映射。从WorldView 2影像中提取建筑物作为辅助数据，然后选择基于建筑物的GWR模型作为最终模型，以将人口计数从管理单位分解到5 m栅格像元上。图像分类的整体准确性为77.75％，但是与基于GWR的土地使用（基于最小二乘的土地）的RMSE值相比，基于建筑物的GWR模型的人口均方根均方根误差（RMSE）显着更低使用和构建模型。我们的模型有潜力适应其他LMIC国家，缺乏高质量的真实人口数据的地方。随着卫星数据的日益增多，本研究开发的方法可以促进在复杂的城市环境中进行高分辨率人口建模，从而改善中低收入国家的人口，社会，环境和健康研究。